Please wait a minute...
FITEE
Front. Inform. Technol. Electron. Eng.  2011, Vol. 12 Issue (8): 638-646    DOI: 10.1631/jzus.C1000355
    
A new artificial bee swarm algorithm for optimization of proton exchange membrane fuel cell model parameters
Alireza Askarzadeh, Alireza Rezazadeh
Faculty of Electrical and Computer Engineering, Shahid Beheshti University, G.C., Evin 1983963113, Tehran, Iran
Download:   PDF(463KB)
Export: BibTeX | EndNote (RIS)      

Abstract  An appropriate mathematical model can help researchers to simulate, evaluate, and control a proton exchange membrane fuel cell (PEMFC) stack system. Because a PEMFC is a nonlinear and strongly coupled system, many assumptions and approximations are considered during modeling. Therefore, some differences are found between model results and the real performance of PEMFCs. To increase the precision of the models so that they can describe better the actual performance, optimization of PEMFC model parameters is essential. In this paper, an artificial bee swarm optimization algorithm, called ABSO, is proposed for optimizing the parameters of a steady-state PEMFC stack model suitable for electrical engineering applications. For studying the usefulness of the proposed algorithm, ABSO-based results are compared with the results from a genetic algorithm (GA) and particle swarm optimization (PSO). The results show that the ABSO algorithm outperforms the other algorithms.

Key words： Proton exchange membrane fuel cell stack model      Parameter optimization      Artificial bee swarm optimization algorithm     
Received: 11 October 2010      Published: 03 August 2011
CLC:  TP301.6  
  TM911  
Service
E-mail this article
Add to my bookshelf
Add to citation manager
E-mail Alert
RSS
Articles by authors
Alireza Askarzadeh
Alireza Rezazadeh
Cite this article:

Alireza Askarzadeh, Alireza Rezazadeh. A new artificial bee swarm algorithm for optimization of proton exchange membrane fuel cell model parameters. Front. Inform. Technol. Electron. Eng., 2011, 12(8): 638-646.

URL:

http://www.zjujournals.com/xueshu/fitee/10.1631/jzus.C1000355     OR     http://www.zjujournals.com/xueshu/fitee/Y2011/V12/I8/638


A new artificial bee swarm algorithm for optimization of proton exchange membrane fuel cell model parameters

An appropriate mathematical model can help researchers to simulate, evaluate, and control a proton exchange membrane fuel cell (PEMFC) stack system. Because a PEMFC is a nonlinear and strongly coupled system, many assumptions and approximations are considered during modeling. Therefore, some differences are found between model results and the real performance of PEMFCs. To increase the precision of the models so that they can describe better the actual performance, optimization of PEMFC model parameters is essential. In this paper, an artificial bee swarm optimization algorithm, called ABSO, is proposed for optimizing the parameters of a steady-state PEMFC stack model suitable for electrical engineering applications. For studying the usefulness of the proposed algorithm, ABSO-based results are compared with the results from a genetic algorithm (GA) and particle swarm optimization (PSO). The results show that the ABSO algorithm outperforms the other algorithms.

关键词： Proton exchange membrane fuel cell stack model,  Parameter optimization,  Artificial bee swarm optimization algorithm 
[1] Lai TENG, Zhong-he JIN. A composite optimization method for separation parameters of large-eccentricity pico-satellites[J]. Front. Inform. Technol. Electron. Eng., 2018, 19(5): 685-698.
[2] Peng Chen, Yong-zai Lu. Extremal optimization for optimizing kernel function and its parameters in support vector regression[J]. Front. Inform. Technol. Electron. Eng., 2011, 12(4): 297-306.